import asyncio
from typing import List

from llama_index.core.agent.workflow import  FunctionAgent
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.indices.vector_store import VectorIndexRetriever
from llama_index.core.storage.chat_store.sql import SQLAlchemyChatStore
from llama_index.core.tools import QueryEngineTool
from llama_index.core.vector_stores import SimpleVectorStore
from llama_index.core.schema import  TextNode
from llama_index.core import Settings, SimpleKeywordTableIndex, SummaryIndex, get_response_synthesizer, \
    VectorStoreIndex, StorageContext
from llama_index.embeddings.zhipuai import ZhipuAIEmbedding
from llama_index.core.graph_stores import SimplePropertyGraphStore
from llama_index.core.schema import Document
from pydantic import BaseModel



embed_model = ZhipuAIEmbedding(
    model="embedding-2",
    api_key="f387f5e4837d4e4bba6d267682a957c9.PmPiTw8qVlsI2Oi5"
    # With the `embedding-3` class
    # of models, you can specify the size
    # of the embeddings you want returned.
    # dimensions=1024
)
Settings.embed_model=embed_model

from llama_index.llms.deepseek import DeepSeek

llm = DeepSeek(model="deepseek-chat", api_key="sk-605e60a1301040759a821b6b677556fb")
Settings.llm = llm


from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import HierarchicalNodeParser
from llama_index.core.retrievers import AutoMergingRetriever

# 1. 文档加载与层次化切割
documents = SimpleDirectoryReader("./data").load_data()
node_parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
nodes = node_parser.get_nodes_from_documents(documents)

print(nodes)

vectorStoreIndex=VectorStoreIndex(nodes=nodes)

base_retriever = VectorIndexRetriever(vectorStoreIndex)
# 2. 构建自动合并检索器

storage_context = StorageContext.from_defaults(
)
retriever = AutoMergingRetriever(
    base_retriever,
    storage_context=storage_context,
    verbose=True
)

# 3. 执行合并检索（当父节点下>50%叶子节点匹配时触发合并）
results = retriever.retrieve("使用环境")
print(results)


